Turning Big Data into Big Insights

Hal Conick
Marketing News
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Key Takeaways

​What? Marketers know about Big Data, but only 29% are able to connect analytics to action.

So what? With $92.2 billion to be invested in Big Data by 2026, marketers must prove ROI by finding insights in their data.

Now what? Marketers should determine what data is actionable, focus on the decisions they can make and keep smart humans employed to find the best insights. 

Jan. 1, 2017

Companies are expected to spend $92 billion on Big Data by 2026, but will they see ROI? If they focus on actionable insights, they can.

Big Data is everywhere in the world of marketing. Symposiums, staff meetings, articles and reports. It’s easier to list business functions where Big Data is not mentioned by marketers at every turn. Numbers cited in a recent blog post by Brian Hopkins, vice president and principal analyst at Forrester Research, lead one to believe that many companies’ focus on Big Data isn’t getting the desired results. Hopkins writes that while 74% of firms say they want to be “data-driven,” only 29% say they are able to connect analytics to action. Worse still, business satisfaction with analytics dropped 21% between 2014 and 2015.

Although companies seem unsatisfied by their Big Data capabilities, they continue to swell the budget with new tools, software and capabilities. Worldwide revenue of the Big Data market is expected to skyrocket from $18.3 billion in 2014 to $92.2 billion by 2026, according to Wikibon. 

Irfan Kamal, vice president of marketing at Aspiration, wrote a 2012 post for Harvard Business Review, titled “Metrics are Easy; Insight is Hard,” that noted how difficult it was for companies to find insights among large swaths of data. Has anything improved in 2016? Kamal isn’t sure many companies are making progress.

Brent Dykes, director of data strategy at Domo, writes in Forbes that actionable insights are the “missing link” between data and business value, the very link Forrester says 71% of companies are missing. This link will be important to advance use of data, as companies are likely going to want to see good ROI on that reported $92 billion Big Data bill.  

Kamal says though Big Data is a huge part of how Aspiration makes decisions, finding value in that data can be time consuming. “Getting to that level is one of the most difficult parts of being a data-driven organization,” Kamal says. 

Here are six tips for marketers to help their companies generate usable, data-based insights. 

1. Determine what’s actionable.

Before hunting for insights, Kamal says marketers must have a clear idea of what is actionable in their business. Companies that travel down the rabbit hole of data exploration will lose time and waste employee energy unless they can set parameters on what they want to achieve. Kamal suggests starting with the problem before searching for insights to improve productivity. 

“Let’s say we’re trying to understand customers or trying to improve some aspect of our conversion funnel,” Kamal says. “We have a pretty clear idea of what we want to do. Then, we look for data that can help us achieve that.” Insights can be very interesting, he says, but to apply them, start with a solid idea of your core business problem and how you think you might be able to solve it.

2. Simplify the process by focusing on decisions.

Marketers can simplify their search for actionable insights by focusing on the important decisions stakeholders or customers will make, according to Bill Schmarzo, CTO of Big Data practice at Dell EMC Global Services.

“Our focus is on the decisions, not the questions,” he says. “Questions are informative, but decisions are actionable. Improving decision making is the best immediate-term way to monetize the organization’s data. Think about the power of improving customer acquisition decisions, such as who to target, with which messages, at what times of day, with what offers, through which channels.”


After marketers identify decisions stakeholders need to make, Schmarzo says companies must identify the metrics that might predict performance. This, he says, is the definition of data science and is critical for success. Companies must remember to focus on the word “might,” he says. There will likely be a large amount of ideas that come from these predictions; emphasizing “might” gives license to be wrong and suggest other ideas that others “might find stupid.”

“During this process, all ideas are worthy of consideration,” Schmarzo says. “Once we have brainstormed the variables and metrics that might be better predictors of performance, then the data science team will apply their techniques and algorithms to actually determine which variables and metrics are better predictors of performance. If you don’t have enough ‘might’ moments, then you’ll never have any breakthrough moments.”

3. Be mindful of stored data.

Ninety percent of data will never be used, according to a prediction from Martin Kihn, research vice president at Gartner. The peril of this over-storage is that marketers lose a level of discipline about what they should be collecting, what they shouldn’t be collecting and why. 

The idea of collecting data in these data lakes is the same idea behind having a single view of the customer, or a comprehensive view of customers including their behavior and attributes. 

“As with Big Data stores, trying to put everything into the customer record can create a kind of overwhelming mess,” Kihn says. “Always start with the end in mind. What do you need to know? What data do you need to answer this question? How can you put it in an available store that is fast enough and query-able for your team? Lead with the question and not with the database.”

4. Keep smart humans employed.

The age of artificial intelligence, machine learning, Big Data and automated processes are giving hope that actionable insights will automatically appear, as if by magic. However, Kamal says finding actionable insights is driven as much by smart humans as it is by actual data.

“The technology we use is fairly basic,” Kamal says. “In the end, humans end up as the most valuable cog in the wheel.”

Kamal has yet to find a magic tool that produces incredible insights, but technology can be helpful to operate in a machine world where things often don’t make sense to humans. However, automation is still riddled with errors and may end up creating more problems than it solves if there are no humans in the loop. Technology is useful, but Kamal says there needs to be creative personnel to develop new ideas, think intelligently about who the best audience is and consider that audience’s receptiveness to new products and services. “That’s very hard to automate. Machines tend to be great at optimization, but at least for now, not as good at coming up with the bigger ideas that can really move the needle in business.”

5. Understand consumer and business needs.

Marketers looking for actionable insights must better understand the challenges faced by customers, Kamal says. This means speaking with those who use the product or service—otherwise known as qualitative insights—before digging for data-based insights.

“We spend a good amount of time using [and] checking accounts and investigating products. And we spend time using our own products,” Kamal says. That means signing up for accounts, looking through sites where Aspiration places ads or organic social and being immersed.​ 

Other ways to get into the customer’s mindset include focus groups, studies and improving the product or offering. The customers’ point of view can also be perceived by looking at the company’s ads with an unbiased eye, using the product or service and trying to figure out where problems may stem from. If the sign-up or log-in process is slow, for example, that’s likely an area where customers will have difficulty. 

Organizations must also understand key business drivers inside their own walls by digging into data and business propositions. Kamal encourages understanding the key differentiators of your business as well as the key barriers preventing people from working with you.

6. Find a data balance.

More data is not always better data. Kamal says companies must use high-quality data to find actionable insights. This means not getting lost in the quantitative data swamp, stepping back and doing “sanity and gut-checks” or speaking with customers for more nuanced, qualitative information. 

Companies must also cut away silos that keep departmental data in feedback loops by getting different people from different disciplines involved. Solving issues as a team, instead of department-by-department, will likely cut down on mistakes and make solving easier.

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Author Bio:

Hal Conick
Hal Conick is a staff writer for the AMA’s magazines and e-newsletters. He can be reached at hconick@ama.org or on Twitter at @HalConick.
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